diff options
Diffstat (limited to 'src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp')
| -rw-r--r-- | src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp | 448 |
1 files changed, 0 insertions, 448 deletions
diff --git a/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp deleted file mode 100644 index f3e47d9ae9..0000000000 --- a/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp +++ /dev/null @@ -1,448 +0,0 @@ -/* - * Copyright (c) 2017-2020 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "arm_compute/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/GLES_COMPUTE/GCHelpers.h" -#include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h" -#include "arm_compute/core/GLES_COMPUTE/IGCTensor.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Validate.h" -#include "support/StringSupport.h" - -using namespace arm_compute; - -template <unsigned int kernel_size> -GCDirectConvolutionLayerKernel<kernel_size>::GCDirectConvolutionLayerKernel() - : _input(nullptr), _bias(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_x(0), _conv_pad_y(0), _lws(gles::NDRange(1U, 1U, 1U)) -{ -} - -template <unsigned int kernel_size> -BorderSize GCDirectConvolutionLayerKernel<kernel_size>::border_size() const -{ - return _border_size; -} - -template <unsigned int kernel_size> -void GCDirectConvolutionLayerKernel<kernel_size>::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *bias, IGCTensor *output, - const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) -{ - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); - ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1)); - ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); - ARM_COMPUTE_ERROR_ON_MSG((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!"); - ARM_COMPUTE_ERROR_ON(kernel_size != weights->info()->dimension(0)); - ARM_COMPUTE_ERROR_ON(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU && act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC); - - if(bias != nullptr) - { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, bias); - // FIXME: Bug in framework, workaround it in tests currently. - //ARM_COMPUTE_ERROR_ON(bias->info()->dimension(0) != weights->info()->dimension(3)); - ARM_COMPUTE_ERROR_ON(bias->info()->num_dimensions() > 1); - } - - // Get convolved dimensions - unsigned int owidth = 0; - unsigned int oheight = 0; - std::tie(owidth, oheight) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_size, kernel_size, conv_info); - - TensorShape output_shape = input->info()->tensor_shape(); - output_shape.set(0, owidth); - output_shape.set(1, oheight); - output_shape.set(2, weights->info()->dimension(3)); - - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type()); - - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_ERROR_ON(!conv_info.padding_is_symmetric()); - - _conv_stride_x = std::get<0>(conv_info.stride()); - _conv_stride_y = std::get<1>(conv_info.stride()); - _conv_pad_x = std::get<0>(conv_info.pad()); - _conv_pad_y = std::get<1>(conv_info.pad()); - - _input = input; - _weights = weights; - _output = output; - _bias = bias; - _border_size = BorderSize(_conv_pad_y, _conv_pad_x); - - std::set<std::string> options; - - options.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(_lws[0])); - options.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(_lws[1])); - options.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(_lws[2])); - options.emplace("#define STRIDE_X " + support::cpp11::to_string(_conv_stride_x)); - options.emplace("#define STRIDE_Y " + support::cpp11::to_string(_conv_stride_y)); - - std::string dt_name = (input->info()->data_type() == DataType::F32) ? "DATA_TYPE_FP32" : "DATA_TYPE_FP16"; - options.emplace(("#define " + dt_name)); - - // Activation information in case of a fused activation - if(act_info.enabled()) - { - options.emplace("#define FUSED_ACTIVATION"); - options.emplace(("#define " + string_from_activation_func(act_info.activation()))); - options.emplace(("#define ACT_OP " + lower_string(string_from_activation_func(act_info.activation())) + "_op")); - options.emplace(("#define A_VAL " + float_to_string_with_full_precision(act_info.a()))); - options.emplace(("#define B_VAL " + float_to_string_with_full_precision(act_info.b()))); - } - - unsigned int num_elems_read_per_iteration_x = kernel_size * _conv_stride_x; - unsigned int num_elems_read_per_iteration_y = 1; - unsigned int num_elems_written_per_iteration_x = 1; - unsigned int num_elems_written_per_iteration_y = 1; - unsigned int num_elems_written_per_iteration_z = 1; - - if(kernel_size == 3) - { - if((_conv_stride_x == 1) && (_conv_stride_y == 1)) - { - switch(input->info()->data_type()) - { - case DataType::F16: - // TODO(APPBROWSER-299): Choose the most optimal path and remove others. -#define PROCESS_4X_3Y_1Z - -#if defined(PROCESS_8X_3Y_1Z) - options.emplace("#define PROCESS_8X_3Y_1Z"); - num_elems_read_per_iteration_x = 16; - num_elems_read_per_iteration_y = 5; - num_elems_written_per_iteration_x = 8; - num_elems_written_per_iteration_y = 3; -#elif defined(PROCESS_4X_3Y_1Z) - options.emplace("#define PROCESS_4X_3Y_1Z"); - num_elems_read_per_iteration_x = 8; - num_elems_read_per_iteration_y = 5; - num_elems_written_per_iteration_x = 4; - num_elems_written_per_iteration_y = 3; -#elif defined(PROCESS_4X_4Y_1Z) - options.emplace("#define PROCESS_4X_4Y_1Z"); - num_elems_read_per_iteration_x = 8; - num_elems_read_per_iteration_y = 6; - num_elems_written_per_iteration_x = 4; - num_elems_written_per_iteration_y = 4; -#elif defined(PROCESS_4X_3Y_2Z) - options.emplace("#define PROCESS_4X_3Y_2Z"); - num_elems_read_per_iteration_x = 8; - num_elems_read_per_iteration_y = 5; - num_elems_written_per_iteration_x = 4; - num_elems_written_per_iteration_y = 3; - num_elems_written_per_iteration_z = 2; -#endif /* PROCESS_nX_nY_nZ */ -#undef PROCESS_8X_3Y_1Z -#undef PROCESS_4X_3Y_1Z -#undef PROCESS_4X_4Y_1Z -#undef PROCESS_4X_3Y_2Z - break; - - case DataType::F32: - options.emplace("#define PROCESS_4X_3Y_1Z"); - num_elems_read_per_iteration_x = 8; - num_elems_read_per_iteration_y = 5; - num_elems_written_per_iteration_x = 4; - num_elems_written_per_iteration_y = 3; - break; - - default: - ARM_COMPUTE_ERROR("Current data type is not supported"); - break; - } - } - // FIXME: Just keep one in release - else - { - switch(input->info()->data_type()) - { - case DataType::F16: - options.emplace("#define PROCESS_4X_1Y_1Z"); - num_elems_read_per_iteration_x = 8; - num_elems_written_per_iteration_x = 4; - break; - - case DataType::F32: - // TODO(APPBROWSER-299): Choose the most optimal path and remove others. -#define PROCESS_4X_1Y_1Z - -#if defined(PROCESS_1X_1Y_1Z) - options.emplace("#define PROCESS_1X_1Y_1Z"); - num_elems_read_per_iteration_x = 3; - num_elems_written_per_iteration_x = 1; -#elif defined(PROCESS_4X_1Y_1Z) - options.emplace("#define PROCESS_4X_1Y_1Z"); - num_elems_read_per_iteration_x = 8; - num_elems_written_per_iteration_x = 4; -#elif defined(PROCESS_8X_1Y_1Z) - options.emplace("#define PROCESS_8X_1Y_1Z"); - num_elems_read_per_iteration_x = 12; - num_elems_written_per_iteration_x = 8; -#else /* PROCESS_nX_nY_nZ */ -#error Have to declare how many elements to process in one thread. -#endif /* PROCESS_nX_nY_nZ */ -#undef PROCESS_1X_1Y_1Z -#undef PROCESS_4X_1Y_1Z -#undef PROCESS_8X_1Y_1Z - break; - - default: - ARM_COMPUTE_ERROR("Current data type is not supported"); - break; - } - } - } - else if(kernel_size == 1) - { - if(weights->info()->dimension(2) % 2 == 0) - { - options.emplace("#define WEIGHTS_OPTIMIZATION"); - } - switch(input->info()->data_type()) - { - case DataType::F16: -#define PROCESS_8X_2Y_1Z - -#if defined(PROCESS_4X_1Y_1Z) - options.emplace("#define PROCESS_4X_1Y_1Z"); - num_elems_read_per_iteration_x = 4; - num_elems_written_per_iteration_x = 4; -#elif defined(PROCESS_4X_2Y_1Z) - options.emplace("#define PROCESS_4X_2Y_1Z"); - num_elems_read_per_iteration_x = 4; - num_elems_read_per_iteration_y = 2; - num_elems_written_per_iteration_x = 4; - num_elems_written_per_iteration_y = 2; -#elif defined(PROCESS_4X_3Y_1Z) - options.emplace("#define PROCESS_4X_3Y_1Z"); - num_elems_read_per_iteration_x = 4; - num_elems_read_per_iteration_y = 3; - num_elems_written_per_iteration_x = 4; - num_elems_written_per_iteration_y = 3; -#elif defined(PROCESS_4X_4Y_1Z) - options.emplace("#define PROCESS_4X_4Y_1Z"); - num_elems_read_per_iteration_x = 4; - num_elems_read_per_iteration_y = 4; - num_elems_written_per_iteration_x = 4; - num_elems_written_per_iteration_y = 4; -#elif defined(PROCESS_4X_2Y_2Z) - ARM_COMPUTE_ERROR_ON_MSG((weights->info()->dimension(4) % 2) == 1, "Current 'weights->info()->dimension(4) % 2) == 1' is not supported"); - options.emplace("#define PROCESS_4X_2Y_2Z"); - num_elems_read_per_iteration_x = 4; - num_elems_read_per_iteration_y = 2; - num_elems_written_per_iteration_x = 4; - num_elems_written_per_iteration_y = 2; - num_elems_written_per_iteration_z = 2; -#elif defined(PROCESS_8X_1Y_1Z) - options.emplace("#define PROCESS_8X_1Y_1Z"); - num_elems_read_per_iteration_x = 8; - num_elems_written_per_iteration_x = 8; -#elif defined(PROCESS_8X_2Y_1Z) - options.emplace("#define PROCESS_8X_2Y_1Z"); - num_elems_read_per_iteration_x = 8; - num_elems_read_per_iteration_y = 2; - num_elems_written_per_iteration_x = 8; - num_elems_written_per_iteration_y = 2; -#else /* PROCESS_4X_1Y_1Z */ -#error Have to declare how many elements to process in one thread. -#endif /* PROCESS_4X_1Y_1Z */ -#undef PROCESS_4X_1Y_1Z -#undef PROCESS_4X_2Y_1Z -#undef PROCESS_4X_3Y_1Z -#undef PROCESS_4X_4Y_1Z -#undef PROCESS_4X_2Y_2Z -#undef PROCESS_8X_1Y_1Z -#undef PROCESS_8X_2Y_1Z - break; - - case DataType::F32: - num_elems_read_per_iteration_x = 1; - num_elems_written_per_iteration_x = 1; - break; - - default: - break; - } - } - else if(kernel_size == 5) - { - switch(input->info()->data_type()) - { - case DataType::F16: - options.emplace("#define PROCESS_4X_1Y_1Z"); - num_elems_read_per_iteration_x = 8; - num_elems_written_per_iteration_x = 4; - - default: - break; - } - } - else - { - } - - if(_bias != nullptr) - { - options.emplace("#define BIAS"); - } - - std::stringstream kernel_name; - kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size; - - _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name.str(), options)); - - unsigned int idx = (_bias == nullptr) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor()); - - // Calculate output right and bottom border - const int output_width = output->info()->dimension(0); - const int output_height = output->info()->dimension(1); - const int output_padding_right = ceil_to_multiple(output_width, num_elems_written_per_iteration_x * _lws[0]) - output_width; - const int output_padding_bottom = ceil_to_multiple(output_height, num_elems_written_per_iteration_y * _lws[1]) - output_height; - - // Calculate input right and bottom border - const int input_width = input->info()->dimension(0); - const int input_height = input->info()->dimension(1); - const int input_total_width = std::max(int(input->info()->padding().left), int(_conv_pad_x)) + input_width + std::max(int(input->info()->padding().right), int(_conv_pad_x)); - const int input_total_height = std::max(int(input->info()->padding().top), int(_conv_pad_y)) + input_height + std::max(int(input->info()->padding().bottom), int(_conv_pad_y)); - const int padding_right1 = ceil_to_multiple(input_total_width, num_elems_read_per_iteration_x * _lws[0]) - input_width - _conv_pad_x; - const int padding_bottom1 = ceil_to_multiple(input_total_height, num_elems_read_per_iteration_y * _lws[1]) - input_height - _conv_pad_y; - - const int upper_bound_w = ceil_to_multiple(((output_width + output_padding_right) * _conv_stride_x + (kernel_size - 1)), num_elems_read_per_iteration_x * _lws[0]) - _conv_pad_x - input_width; - const int upper_bound_h = ceil_to_multiple(((output_height + output_padding_bottom) * _conv_stride_y + (kernel_size - 1)), num_elems_read_per_iteration_y * _lws[1]) - _conv_pad_y - input_height; - const int padding_right2 = std::max(upper_bound_w, _conv_pad_x); - const int padding_bottom2 = std::max(upper_bound_h, _conv_pad_y); - - const int padding_right = std::max(padding_right1, padding_right2); - const int padding_bottom = std::max(padding_bottom1, padding_bottom2); - - BorderSize border = BorderSize(0, output_padding_right, output_padding_bottom, 0); - - Window win = calculate_max_enlarged_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y, num_elems_written_per_iteration_z), border); - - AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom); - AccessWindowStatic weights_access = AccessWindowStatic(nullptr, 0, 0, 0, 0); - AccessWindowStatic bias_access = AccessWindowStatic(nullptr, 0, 0, 0, 1); - - switch(weights->info()->data_type()) - { - case DataType::F16: - if((weights->info()->dimension(2) % 2 != 0) || (kernel_size != 1)) - { - weights_access = AccessWindowStatic(weights->info(), 0, 0, kernel_size + 1, kernel_size); - } - if(_bias != nullptr) - { - bias_access = AccessWindowStatic(_bias->info(), 0, 0, _bias->info()->dimension(0) + 1, 1); - } - break; - - case DataType::F32: - weights_access = AccessWindowStatic(weights->info(), 0, 0, kernel_size, kernel_size); - if(_bias != nullptr) - { - bias_access = AccessWindowStatic(_bias->info(), 0, 0, _bias->info()->dimension(0), 1); - } - break; - - default: - ARM_COMPUTE_ERROR("Current data type is not supported"); - break; - } - - AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom); - - if(_bias != nullptr) - { - update_window_and_padding(win, input_access, weights_access, bias_access, output_access); - } - else - { - update_window_and_padding(win, input_access, weights_access, output_access); - } - - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); - - _kernel.set_argument(idx++, _weights->info()->strides_in_bytes()[3]); // weights_stride_w - _kernel.set_argument(idx++, _weights->info()->dimension(2)); // weights_depth - - IGCKernel::configure(win); -} - -template <unsigned int kernel_size> -void GCDirectConvolutionLayerKernel<kernel_size>::run(const Window &window) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - - _kernel.use(); - - _output->set_needs_shifting(true); - - // Get initial windows - Window slice = window.first_slice_window_3D(); - Window win_in = window; - - win_in.adjust(Window::DimX, -_conv_pad_x, true); - win_in.adjust(Window::DimY, -_conv_pad_y, true); - win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x); - win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y); - - Window slice_in = win_in.first_slice_window_3D(); - - unsigned int idx1 = 2 * num_arguments_per_3D_tensor(); - add_3D_tensor_argument(idx1, _weights, 3, slice); - - if(_bias != nullptr) - { - Window slice_bias; - slice_bias.use_tensor_dimensions(_bias->info()->tensor_shape()); - add_1D_tensor_argument(idx1, _bias, 4, slice_bias); - } - - slice.shift(Window::DimX, -(_output->info()->padding()).left); - - do - { - unsigned int idx = 0; - - add_3D_tensor_argument(idx, _input, 1, slice_in); - add_3D_tensor_argument(idx, _output, 2, slice); - - _kernel.update_shader_params(); - enqueue(*this, slice, _lws); - } - while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in)); -} - -template class arm_compute::GCDirectConvolutionLayerKernel<1>; -template class arm_compute::GCDirectConvolutionLayerKernel<3>; -template class arm_compute::GCDirectConvolutionLayerKernel<5>; |